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  <meta name="description" content="这个需要和BTree一起来讲，我们都知道BTree是Balance Tree，为了维持Balancing的特性，每次读入的时候需要对树进行调整，是一笔不小的时间损耗。在一些使用场景下（例如google爬取网页），需要高性能写入，而对读的要求并不是那么高。因此，LSM树是基于这样的背景下发展出来的，针对类似的场景LSM提出了很多优化措施。 应用场景举几个比较常见的例子：HBase、LevelDB、R">
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          LSM树的不同实现介绍
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        <p>这个需要和BTree一起来讲，我们都知道BTree是Balance Tree，为了维持Balancing的特性，每次读入的时候需要对树进行调整，是一笔不小的时间损耗。在一些使用场景下（例如google爬取网页），需要高性能写入，而对读的要求并不是那么高。<br>因此，LSM树是基于这样的背景下发展出来的，针对类似的场景LSM提出了很多优化措施。</p>
<h2 id="应用场景"><a href="#应用场景" class="headerlink" title="应用场景"></a>应用场景</h2><p>举几个比较常见的例子：HBase、LevelDB、RocksDB</p>
<blockquote>
<p>HBase大家可能比较熟，LevelDB和RocksDB分别是Google和FaceBook推出的，同样也是开源的。对于英语好的同学可以直接看看官方的文档，对于LSM的工业实现会有一个更深入的理解。</p>
<p>为什么要看不同的DB的实现呢？因为虽然是同个数据结构，但是在实现上通过类似布隆过滤器等手段，不同的实现方式能够大大提升其效率，因此了解不同公司的LSM树实现还是很有必要的。</p>
</blockquote>
<h2 id="LSM树基础"><a href="#LSM树基础" class="headerlink" title="LSM树基础"></a>LSM树基础</h2><p>LSM Tree，全写Log-Structured Merge Tree。本质上通过顺序写入log的方式，大大的提升写入的性能，同时NoSQL的结构属性帮助其天然适合Shard。</p>
<h3 id="基本操作"><a href="#基本操作" class="headerlink" title="基本操作"></a>基本操作</h3><p>1.将数据写入内存memtable（同时硬盘中复制一份，保证系统宕机数据不丢失）<br>2.数据到达一定容量，将数据flush到硬盘SStable，同时对硬盘中的数据进行merge</p>
<h3 id="复杂之处-Compaction"><a href="#复杂之处-Compaction" class="headerlink" title="复杂之处:Compaction"></a>复杂之处:Compaction</h3><p>SStable在硬盘中不断增多，会带来读的巨大压力。这时候就需要“数据结构”所独特的价值之处了，通过不同的Compaction(压缩)来构建不同的数据结构，能够有效的减低读的效率。<br>用空间来换取时间，这也是HBase、LevelDB、RocksDB三者之间的真正区别所在。</p>
<h2 id="HBase中的LSM实现"><a href="#HBase中的LSM实现" class="headerlink" title="HBase中的LSM实现"></a>HBase中的LSM实现</h2><p>先来看下HBase中的实现，如图所示。</p>
<img src="https://olwr1lamu.qnssl.com/lsm.png" alt="hbase_lsm"/>
网络上很多提到了Tree的概念，其实HBase的实现并没有依托于Tree。大家肯定有个疑问，“HBase那不是会很慢？”事实上肯定不慢，正是靠着布隆过滤器，HBase很好的诠释了数据结构时间和空间之间的艺术。

<h3 id="布隆过滤器"><a href="#布隆过滤器" class="headerlink" title="布隆过滤器"></a>布隆过滤器</h3><p>布隆过滤器和动态缓存有点像，不过都是数据结构很典型的例子，使用空间大幅度的降低了时间的使用。<br><img src="https://olwr1lamu.qnssl.com/bloom.png" alt="bloom"/></p>
<p>bloom 借助的是一个大大大的位表以及多个hash函数，例如图中，将{x,y,z}的用3个hash函数映射到位表中。那么位表中有n个位置有标1,（3&lt;=n&lt;=9）。</p>
<ol>
<li>如果这个时候来了个w，w经过3个hash函数，对应到位表上的位置有一个映射的值为0，那么说明w肯定不在{x,y,x}中。</li>
<li>如果w的3个hash函数映射后，都为1，并不能够说明w在{x,y,z}中。有可能是类似上图4，5，6位置格，而那些格是有不同的值映射出来的。</li>
</ol>
<h3 id="HBase的Compaction"><a href="#HBase的Compaction" class="headerlink" title="HBase的Compaction"></a>HBase的Compaction</h3><p>如同上文所讲，Compaction才是一个LSM树的核心。HBase中分为两种：</p>
<ul>
<li>Minor Compaction</li>
<li>Major Compaction</li>
</ul>
<p>两者都是合并SStable(在HBase中SStable叫HFile)。Major Compaction在合并的时候回删除一些过期的Key，持续时间比较长，一般在业务低峰期手动触发。（业务低峰期可使用hbase.offpeak.start.hour和hbase.offpeak.end.hour配置）</p>
<p>同时，不同版本的HBase也提供了不同的Compaction策略让使用者来决策:</p>
<ul>
<li>RatioBasedCompactionPolicy</li>
<li>ExploringCompactionPolicy</li>
</ul>
<blockquote>
<p>两种具体介绍看:<a href="http://hbasefly.com/2016/07/13/hbase-compaction-1/">http://hbasefly.com/2016/07/13/hbase-compaction-1/</a></p>
</blockquote>
<p>因为笔者没有实际使用过HBase的经验，所以不敢大加妄论，各位看官可以通过上面的连接看看比较专业的介绍。但是，对于笔者这样的业务方而言，HBase没有提供索引，而是通过Scanner去查询数据，似乎这个数据结构不够“完美”。</p>
<h2 id="LevelDB-and-RocksDB"><a href="#LevelDB-and-RocksDB" class="headerlink" title="LevelDB and RocksDB"></a>LevelDB and RocksDB</h2><p>RocksDb是facebook团队基于google开发的LevelDB的升级版本，提供了很多LevelDB所不具备的feature。（延伸一下，TiDB是一个newSQL数据库，它的底层就是基于RocksDb。网上已经有很多公司已经将TiDB上到生产环境，笔者所在公司的数据团队也已经灰度的一部分数据到TiDB上了。说明RocksDb也是挺可靠的。）<br>LevelDB的文档比较少，所以一起来看RocksDB的实现吧。</p>
<blockquote>
<p>LevelDB文档地址: <a href="https://github.com/google/leveldb/blob/master/doc/impl.md">https://github.com/google/leveldb/blob/master/doc/impl.md</a><br>RocksDB文档地址: <a href="https://github.com/facebook/rocksdb/wiki">https://github.com/facebook/rocksdb/wiki</a></p>
</blockquote>
<h3 id="Compactions"><a href="#Compactions" class="headerlink" title="Compactions"></a>Compactions</h3><p>RocksDB也提供了多种的策略:</p>
<ul>
<li>Leveled style compaction</li>
<li>Universal style compaction</li>
<li>FIFO Compaction </li>
</ul>
<p>这里主要介绍下Leveled style compaction，因为他是起源于LevelDB，方便大家和LevelDB一同了解。</p>
<h3 id="Leveled-style-compaction"><a href="#Leveled-style-compaction" class="headerlink" title="Leveled style compaction"></a>Leveled style compaction</h3><img src="https://olwr1lamu.qnssl.com/rocksdb1.jpg" alt="rocksdb1"/>

<p>如上图，可以看到Leveled style将SStable划分为不同的Level，除了Level 1中可能存在重复的key之外，Level 2之后都不会有重复的key。同时每个SStable中，key都是有序的，不要小看了有序的作用，看到查找你就会知道他的妙用。</p>
<img src="https://olwr1lamu.qnssl.com/rocksdb2.jpg" alt="rocksdb2"/>
另外，每一层的容量是递增的，这个也比较好理解，如上图。

<img src="https://olwr1lamu.qnssl.com/rocksdb3.jpg" alt="rocksdb3"/>

<p>如上图，compaction的过程其实也比较简单，当某一个level的数量大于容量的时候，会选一个SStable与level+1的数据进行merge。选取的level+1的SStable是存在和当前SStable存在相同key的。如果生成的SStable导致level+1的容量也超过限度，那么继续往下merge。</p>
<p>比较吊的是，除了level0和level1的merge，其他的merge都是可以多线程一起进行的，因为是没有重复key的，所以不用担心并发问题。</p>
<h3 id="查找"><a href="#查找" class="headerlink" title="查找"></a>查找</h3><p>就像上面说的有序性能够极大的提升key查找的效率，查找通过两步:</p>
<ol>
<li>二分查找所有文件的start和end，找到可能存在key的file</li>
<li>二分查找file中所有包含的key</li>
</ol>
<p>所有的查找都是通过二分查找来实现的。</p>
<h2 id="总结"><a href="#总结" class="headerlink" title="总结"></a>总结</h2><p>本文从LSM介绍入手，概览了一些LSM的工程实现。由于还没深入了解这几种DB的源码，仅仅只能从文档中找到一些有价值的内容给大家看，希望能够抛砖迎玉，让大家重新体会到数据结构的妙用。如有内容不对或者想交流一下，请随时联系我~</p>

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